METHODS: The validated Malay version of the Job Content Questionnaire (JCQ), Depression Anxiety Stress Scales (DASS) and the World Health Organization Quality of Life-Brief (WHOQOL-BREF) were used. A structural equation modelling (SEM) analysis was applied to test the structural relationships of the model using AMOS version 6.0, with the maximum likelihood ratio as the method of estimation.
RESULTS: The results of the SEM supported the hypothesized structural model (chi2 = 22.801, df = 19, p = 0.246). The final model shows that social support (JCQ) was directly related to all 4 factors of the WHOQOL-BREF and inversely related to depression and stress (DASS). Job demand (JCQ) was directly related to stress (DASS) and inversely related to the environmental conditions (WHOQOL-BREF). Job control (JCQ) was directly related to social relationships (WHOQOL-BREF). Stress (DASS) was directly related to anxiety and depression (DASS) and inversely related to physical health, environment conditions and social relationships (WHOQOL-BREF). Anxiety (DASS) was directly related to depression (DASS) and inversely related to physical health (WHOQOL-BREF). Depression (DASS) was inversely related to the psychological wellbeing (WHOQOL-BREF). Finally, stress, anxiety and depression (DASS) mediate the relationships between job demand and social support (JCQ) to the 4 factors of WHOQOL-BREF.
CONCLUSION: These findings suggest that higher social support increases the self-reported quality of life of these workers. Higher job control increases the social relationships, whilst higher job demand increases the self-perceived stress and decreases the self-perceived quality of life related to environmental factors. The mediating role of depression, anxiety and stress on the relationship between working conditions and perceived quality of life in automotive workers should be taken into account in managing stress amongst these workers.
METHODS: The study enrolled 110 participants (age: M = 46.85, SD = 11.23; female: 55.45%) undergoing hospital treatment, of whom 87 were included in the pre-post analysis. Participants completed a German translation of MAIA-2 and the Beck Depression Inventory-II (pre-/post-treatment). Internal consistency reliability was determined by Cronbach's α/McDonalds's ω, sensitivity to change was determined by effect sizes, and MIDs were determined by distribution- (0.5*SD) and anchor-based approaches (mean change method; ROC curve cut-points).
RESULTS: Depression severity reduced over the course of treatment (Median = -65.22%), and 34.48% of patients achieved remission. Reliability was appropriate for post-treatment (range of ω: .70-.90), but questionable for two pre-treatment scales (Noticing: ω = .64; Not-Distracting: ω = .66). The eight dimensions of MAIA-2 were sensitive to change (standardized response mean: .32-.81; Cohen's effect size: .30-.92). Distribution-based MIDs (.38-.61) and anchor-based mean change MIDs (remission vs. partial response: .00-.85; partial response vs. nonresponse: .08-.88) were established on the group level. For six scales, ROC cut-points (remission: .00-1.33; response: -.20-1.00) demonstrated accurate classification to treatment response groups on the individual level.
CONCLUSIONS: This study demonstrated the applicability of the MAIA-2 questionnaire in MDD. The updated version may have led to reliability improvements regarding the revised scales, but subthreshold reliability was evident prior to treatment. The measure's dimensions were sensitive to change. MIDs were established that corresponded with antidepressive treatment outcomes. Our findings are consistent with a growing area of research which considers somatic feelings as key contributors to mental health.
Method: Potential studies were identified through a systematic search of Scopus, Science Direct, Google Scholar, and PubMed. The keywords used to identify relevant articles were "adherence," "AED," "epilepsy," "non-adherence," and "complementary and alternative medicine." An article was included in the review if the study met the following criteria: 1) conducted in epilepsy patients, 2) conducted in patients aged 18 years and above, 3) conducted in patients prescribed AEDs, and 4) patients' adherence to AEDs.
Results: A total of 3,330 studies were identified and 30 were included in the final analysis. The review found that the AED non-adherence rate reported in the studies was between 25% and 66%. The percentage of CAM use was found to be between 7.5% and 73.3%. The most common reason for inadequate AED therapy and higher dependence on CAM was the patients' belief that epilepsy had a spiritual or psychological cause, rather than primarily being a disease of the brain. Other factors for AED non-adherence were forgetfulness, specific beliefs about medications, depression, uncontrolled recent seizures, and frequent medication dosage.
Conclusion: The review found a high prevalence of CAM use and non-adherence to AEDs among epilepsy patients. However, a limited number of studies have investigated the association between CAM usage and AED adherence. Future studies may wish to explore the influence of CAM use on AED medication adherence.